\(\bullet \)  Sparsity.
High dimensional linear bandits with sparsity capture practical cases such as, when there exist a large number of candidate features and limited information on which of them are useful; use cases include personalized medicine and online advertising [1]}.
This setup results in sparsity in the unknown linear bandit parameters, which can be leveraged for more efficient learning.
